

import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:					Optional[int]		 =   logging.get_logger(__name__)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[Any]			,a__    :   List[Any]			,a__    :   List[Any]       )					->				List[Any]:
	__A :			List[str]		    =			os.path.abspath(a__       )
	logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""       )
	# Load weights from TF model
	__A :			Optional[Any]		    =			tf.train.list_variables(a__       )
	__A :			Union[str, Any]		    =			[]
	__A :			List[str]		    =			[]
	__A :			List[Any]		    =			[]
	for full_name, shape in init_vars:
		# logger.info(f"Loading TF weight {name} with shape {shape}")
		__A :			List[str]		    =			full_name.split("""/"""       )
		if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
			logger.info(f"""Skipping non-model layer {full_name}"""       )
			continue
		if "optimizer" in full_name:
			logger.info(f"""Skipping optimization layer {full_name}"""       )
			continue
		if name[0] == "model":
			# ignore initial 'model'
			__A :			int		    =			name[1:]
		# figure out how many levels deep the name is
		__A :			Optional[Any]		    =			0
		for _name in name:
			if _name.startswith("""layer_with_weights"""       ):
				depth += 1
			else:
				break
		layer_depth.append(a__       )
		# read data
		__A :			Optional[Any]		    =			tf.train.load_variable(a__			,a__       )
		names.append("""/""".join(a__       )       )
		arrays.append(a__       )
	logger.info(f"""Read a total of {len(a__       ):,} layers"""       )

	# Sanity check
	if len(set(a__       )       ) != 1:
		raise ValueError(f"""Found layer names with different depths (layer depth {list(set(a__       )       )})"""       )
	__A :			Union[str, Any]		    =			list(set(a__       )       )[0]
	if layer_depth != 1:
		raise ValueError(
		    """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
		    """ heads."""       )

	# convert layers
	logger.info("""Converting weights..."""       )
	for full_name, array in zip(a__			,a__       ):
		__A :			Optional[Any]		    =			full_name.split("""/"""       )
		__A :			Any		    =			model
		__A :			List[str]		    =			[]
		for i, m_name in enumerate(a__       ):
			if m_name == ".ATTRIBUTES":
				# variable names end with .ATTRIBUTES/VARIABLE_VALUE
				break
			if m_name.startswith("""layer_with_weights"""       ):
				__A :			Tuple		    =			int(m_name.split("""-"""       )[-1]       )
				if layer_num <= 2:
					# embedding layers
					# layer_num 0: word_embeddings
					# layer_num 1: position_embeddings
					# layer_num 2: token_type_embeddings
					continue
				elif layer_num == 3:
					# embedding LayerNorm
					trace.extend(["""embeddings""", """LayerNorm"""]       )
					__A :			Any		    =			getattr(a__			,"""embeddings"""       )
					__A :			int		    =			getattr(a__			,"""LayerNorm"""       )
				elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
					# encoder layers
					trace.extend(["""encoder""", """layer""", str(layer_num - 4       )]       )
					__A :			Dict		    =			getattr(a__			,"""encoder"""       )
					__A :			Dict		    =			getattr(a__			,"""layer"""       )
					__A :			Optional[Any]		    =			pointer[layer_num - 4]
				elif layer_num == config.num_hidden_layers + 4:
					# pooler layer
					trace.extend(["""pooler""", """dense"""]       )
					__A :			List[str]		    =			getattr(a__			,"""pooler"""       )
					__A :			str		    =			getattr(a__			,"""dense"""       )
			elif m_name == "embeddings":
				trace.append("""embeddings"""       )
				__A :			Optional[Any]		    =			getattr(a__			,"""embeddings"""       )
				if layer_num == 0:
					trace.append("""word_embeddings"""       )
					__A :			Any		    =			getattr(a__			,"""word_embeddings"""       )
				elif layer_num == 1:
					trace.append("""position_embeddings"""       )
					__A :			Optional[int]		    =			getattr(a__			,"""position_embeddings"""       )
				elif layer_num == 2:
					trace.append("""token_type_embeddings"""       )
					__A :			List[Any]		    =			getattr(a__			,"""token_type_embeddings"""       )
				else:
					raise ValueError(f"""Unknown embedding layer with name {full_name}"""       )
				trace.append("""weight"""       )
				__A :			int		    =			getattr(a__			,"""weight"""       )
			elif m_name == "_attention_layer":
				# self-attention layer
				trace.extend(["""attention""", """self"""]       )
				__A :			Union[str, Any]		    =			getattr(a__			,"""attention"""       )
				__A :			List[Any]		    =			getattr(a__			,"""self"""       )
			elif m_name == "_attention_layer_norm":
				# output attention norm
				trace.extend(["""attention""", """output""", """LayerNorm"""]       )
				__A :			List[str]		    =			getattr(a__			,"""attention"""       )
				__A :			int		    =			getattr(a__			,"""output"""       )
				__A :			List[Any]		    =			getattr(a__			,"""LayerNorm"""       )
			elif m_name == "_attention_output_dense":
				# output attention dense
				trace.extend(["""attention""", """output""", """dense"""]       )
				__A :			Union[str, Any]		    =			getattr(a__			,"""attention"""       )
				__A :			List[str]		    =			getattr(a__			,"""output"""       )
				__A :			Optional[Any]		    =			getattr(a__			,"""dense"""       )
			elif m_name == "_output_dense":
				# output dense
				trace.extend(["""output""", """dense"""]       )
				__A :			List[Any]		    =			getattr(a__			,"""output"""       )
				__A :			int		    =			getattr(a__			,"""dense"""       )
			elif m_name == "_output_layer_norm":
				# output dense
				trace.extend(["""output""", """LayerNorm"""]       )
				__A :			int		    =			getattr(a__			,"""output"""       )
				__A :			Optional[int]		    =			getattr(a__			,"""LayerNorm"""       )
			elif m_name == "_key_dense":
				# attention key
				trace.append("""key"""       )
				__A :			Union[str, Any]		    =			getattr(a__			,"""key"""       )
			elif m_name == "_query_dense":
				# attention query
				trace.append("""query"""       )
				__A :			str		    =			getattr(a__			,"""query"""       )
			elif m_name == "_value_dense":
				# attention value
				trace.append("""value"""       )
				__A :			List[str]		    =			getattr(a__			,"""value"""       )
			elif m_name == "_intermediate_dense":
				# attention intermediate dense
				trace.extend(["""intermediate""", """dense"""]       )
				__A :			str		    =			getattr(a__			,"""intermediate"""       )
				__A :			Union[str, Any]		    =			getattr(a__			,"""dense"""       )
			elif m_name == "_output_layer_norm":
				# output layer norm
				trace.append("""output"""       )
				__A :			Optional[Any]		    =			getattr(a__			,"""output"""       )
			# weights & biases
			elif m_name in ["bias", "beta"]:
				trace.append("""bias"""       )
				__A :			int		    =			getattr(a__			,"""bias"""       )
			elif m_name in ["kernel", "gamma"]:
				trace.append("""weight"""       )
				__A :			Any		    =			getattr(a__			,"""weight"""       )
			else:
				logger.warning(f"""Ignored {m_name}"""       )
        # for certain layers reshape is necessary
		__A :			List[Any]		    =			""".""".join(a__       )
		if re.match(r"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""			,a__       ) or re.match(
		    r"""(\S+)\.attention\.output\.dense\.weight"""			,a__       ):
			__A :			int		    =			array.reshape(pointer.data.shape       )
		if "kernel" in full_name:
			__A :			int		    =			array.transpose()
		if pointer.shape == array.shape:
			__A :			Dict		    =			torch.from_numpy(a__       )
		else:
			raise ValueError(
			    f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
			    f""" {array.shape}"""       )
		logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""       )
	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   str			,a__    :   Optional[int]       )					->				Optional[Any]:
	# Instantiate model
	logger.info(f"""Loading model based on config from {config_path}..."""       )
	__A :			Tuple		    =			BertConfig.from_json_file(a__       )
	__A :			Optional[int]		    =			BertModel(a__       )

	# Load weights from checkpoint
	logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""       )
	load_tfa_weights_in_bert(a__			,a__			,a__       )

	# Save pytorch-model
	logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Optional[Any]		 =   argparse.ArgumentParser()
			parser.add_argument(
			    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
			)
			parser.add_argument(
			    '''--bert_config_file''',
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''',
			    type=str,
			    required=True,
			    help='''Path to the output PyTorch model (must include filename).''',
			)
			UpperCAmelCase_							:					Union[str, Any]		 =   parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case : Tuple     		=		None

snake_case : Tuple     		=		logging.get_logger(__name__)
snake_case : List[Any]     		=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case : Dict     		=		{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : int     		=		{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


snake_case : List[str]     		=		"""▁"""



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = BigBirdTokenizer
    __UpperCAmelCase           = ["""input_ids""", """attention_mask"""]
    __UpperCAmelCase           = []







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	int="<unk>"				,    UpperCamelCase_ :	Tuple="<s>"				,    UpperCamelCase_ :	int="</s>"				,    UpperCamelCase_ :	Union[str, Any]="<pad>"				,    UpperCamelCase_ :	Optional[Any]="[SEP]"				,    UpperCamelCase_ :	int="[MASK]"				,    UpperCamelCase_ :	Tuple="[CLS]"				,    **UpperCamelCase_ :	Optional[Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else bos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else eos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else unk_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else pad_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cls_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else mask_token

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    vocab_file
           __magic_name__      				=    False if not self.vocab_file else True







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None				,    UpperCamelCase_ :	bool = False ):





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.' )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase_ )) + [1]
           return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ )) + [1]







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.' )

           if not os.path.isdir(UpperCamelCase_ ):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                  return
           __magic_name__      				=    os.path.join(
               UpperCamelCase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                  copyfile(self.vocab_file				,    UpperCamelCase_ )

           return (out_vocab_file,)